Risk assessment and early prediction of intraventricular hemorrhage in extremely preterm infants

• Published in: Scientific reports Vol. 15; no. 1; pp. 17346 - 10
• Main Authors: Cai, Yueju, Li, Xiaolan, Zhao, Xiaopeng, Song, Yanyan, Zhou, Wei
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 19.05.2025; Nature Publishing Group; Nature Portfolio
• Subjects: 692/499; 692/699/375/1345/3195; Cerebral Hemorrhage - diagnosis; Cerebral Hemorrhage - epidemiology; Cerebral Hemorrhage - etiology; Cerebral Intraventricular Hemorrhage - diagnosis; Cerebral Intraventricular Hemorrhage - epidemiology; Cerebral Intraventricular Hemorrhage - etiology; Extreme preterm infants; Female; Gestational Age; Hemorrhage; Humanities and Social Sciences; Humans; Infant, Extremely Premature; Infant, Newborn; Infants; Intraventricular hemorrhage; Male; Mechanical ventilation; multidisciplinary; Neonatal care; Neonates; Newborn babies; Prediction models; Predictive model; Premature babies; Respiration, Artificial - adverse effects; Retrospective Studies; Risk Assessment; Risk Factors; Risk groups; Science; Science (multidisciplinary); Intraventricular hemorrhage; Neonatal care; Extreme preterm infants; Risk factors; Predictive model
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-025-02061-4

This study aimed to identify the risk factors associated with intraventricular hemorrhage (IVH) in extremely preterm infants (EPIs), focusing on early-stage prediction to improve clinical outcomes. A retrospective cohort study was conducted at Guangzhou Women and Children’s Medical Center, including 189 EPIs born between January 2019 and December 2023. Infants were categorized into IVH and non-IVH groups based on head ultrasound findings. Risk factors were assessed using univariate and multivariate analyses, and a predictive model for IVH was developed. Of the 189 EPIs, 80 (42.3%) developed IVH, with 26 (13.8%) experiencing severe IVH. Gestational age was identified as a significant protective factor (OR = 0.565, p  = 0.023), while invasive mechanical ventilation (IMV) was a key risk factor (OR = 2.718, p  = 0.012). The predictive model demonstrated good performance, with an AUC of 0.753 (95% CI: 0.681–0.825). Gestational age and IMV are critical factors in the development of IVH in EPIs. Early identification of high-risk infants based on these factors can aid in timely interventions to reduce IVH incidence and improve outcomes.